Vertical-horizontal Research Articles

Using SAR imagery to extract flash flood sediment deposition area in the northern Loess Plateau

The lack of hydrology data brings challenges to the accurate simulation of the inundation range of flash floods using hydrodynamic models. In addition, the water bodies of flash floods often disappear quickly, which makes it difficult for remote sensing satellites to extract flash flood inundated areas without standing water. There is currently no research using flood sediment deposition areas to characterize flash floods in areas without water accumulation. To address this research gap, we developed a method based on Google Earth Engine (GEE) that uses Sentinel-1 SAR imagery to automatically extract flash flood sediment deposition areas. This method, termed Power Operation of Different Polarization Combination (PODPC), differs from other feature extraction methods in that it utilizes not only the original Vertical-Vertical (VV) and Vertical-Horizontal (VH) polarizations and their simple combinations but also employs power operations and threshold techniques to extrac t sediment deposition areas. We explored the advanced features of PODPC in terms of extraction accuracy, threshold segmentation effect, and sediment deposition area distribution. The results demonstrate that the histograms generated by PODPC exhibit clearer bimodal characteristics, facilitating better threshold selection. VV3, (VV + VH)6, and (VV × VH)3 produced fewer false deposition areas, achieving an overall accuracy of 92 % ∼ 93 %. Compared to commonly used methods such as VV, VH, and VV + VH, PODPC increased the overall accuracy by an average of 23 %. (VV-VH)n and (VV/VH)n are are considered unsuitable for the extraction of sediment deposition areas. The extracted sediment deposition area can serve as indicator of flash flood severity and as validation and input data for flash flood models, enhancing the scientific assessment of flash flood disasters.

An Optimized Semi-Supervised Generative Adversarial Network Rice Extraction Method Based on Time-Series Sentinel Images

In response to the limitations of meteorological conditions in global rice growing areas and the high cost of annotating samples, this paper combines the Vertical-Vertical (VV) polarization and Vertical-Horizontal (VH) polarization backscatter features extracted from Sentinel-1 synthetic aperture radar (SAR) images and the NDVI, NDWI, and NDSI spectral index features extracted from Sentinel-2 multispectral images. By leveraging the advantages of an optimized Semi-Supervised Generative Adversarial Network (optimized SSGAN) in combining supervised learning and semi-supervised learning, rice extraction can be achieved with fewer annotated image samples. Within the optimized SSGAN framework, we introduce a focal-adversarial loss function to enhance the learning process for challenging samples; the generator module employs the Deeplabv3+ architecture, utilizing a Wide-ResNet network as its backbone while incorporating dropout layers and dilated convolutions to improve the receptive field and operational efficiency. Experimental results indicate that the optimized SSGAN, particularly when utilizing a 3/4 labeled sample ratio, significantly improves rice extraction accuracy, leading to a 5.39% increase in Mean Intersection over Union (MIoU) and a 2.05% increase in Overall Accuracy (OA) compared to the highest accuracy achieved before optimization. Moreover, the integration of SAR and multispectral data results in an OA of 93.29% and an MIoU of 82.10%, surpassing the performance of single-source data. These findings provide valuable insights for the extraction of rice information in global rice-growing regions.

A High-Resolution Distribution Dataset of Paddy Rice in India Based on Satellite Data

India, as the world’s second-largest rice producer, accounting for 21.7% of global rice production, plays a crucial role in ensuring global food supply stability. However, creating high-resolution rice maps for India, such as those at 10 to 30 m, poses significant challenges due to frequent cloudy weather conditions and the complexities of its agricultural systems. This study used a sample-independent mapping method for rice in India using the synthetic aperture radar (SAR)-based Rice Index (SPRI). We produced 10 m spatial resolution rice distribution maps for three years (i.e., 2018, 2020, and 2022) for 23 states in India, covering 98% of Indian rice production. The method effectively utilized the unique characteristics of rice in the vertical–horizontal (VH) backscatter coefficient time series of Sentinel-1, from ttransplantation to the maturity stage, combined with cloud-free Sentinel-2 imagery. By calculating the SPRI values for each agricultural field object using adaptive parameters, the planting locations of rice were accurately identified. On average, the user, producer, and overall accuracy over all investigated states and union territories was 84.72%, 82.31%, and 84.40%, respectively. Additionally, the regional-scale validation based on the statistical area at the district level showed that the coefficient of determination (R2) ranged from 0.53 to 0.95 for each state, indicating that the spatial distribution of the statistical planted area at the district level was reproduced well.

Wind speed retrieval approach for VV-polarized synthetic aperture radar during tropical cyclone based on XGBoost model

ABSTRACT In this study, a machine learning approach is applied to wind speed retrieval from vertical-vertical (VV) polarized synthetic aperture radar (SAR) during tropical cyclones (TC). More than 2400 dual-polarized (VV and vertical-horizontal (VH)) Sentinel-1 (S-1) images acquired in interferometric wide (IW) and extra wide (EW) mode are collected in 2016–2022. Along-track observations from stepped-frequency microwave radiometer (SFMR) and soil moisture active passive (SMAP) are available for several images. The azimuthal cut-off wavelength (ACW) is derived from VV-polarized image and the Doppler centroid anomaly (DCA) is provided from Ocean (OCN) product. It is found that SFMR wind speed linearly relates with ACW and DCA. Following this rationale, a machine learning method, called eXtreme Gradient Boosting (XGBoost), is trained for constructing SAR wind speed retrieval geophysical model function (GMF) through abundant matchups from 2000 images collocated with wind speeds from soil moisture active passive (SMAP) microwave radiometer and SFMR, denoted as TCWIND2-S1. Then GMF TCWIND2-S1 is applied for more than 400 images and the validation against SMAP and SFMR products up to 70 m s−1 shows a 3.01 m s−1 root mean squared error (RMSE) with a 0.16 scatter index (SI) and a 0.92 correlation (COR). Retrieval is also compared with CyclObs wind products derived from more than 400 dual-polarized images, yielding a 2.66 m s−1 RMSE with a 0.19 SI and a 0.93 COR. Our work provides an approach for VV-polarized SAR wind retrieval at wind speed ranged from 0 to 70 m s−1 during TCs. This work is conveniently adopted for TerraSAR-X without operating at cross polarization channel.

Probabilistic assessment of bearing capacity of skirted foundation under combined loadings with a rigid base

Skirted foundations are critical components in offshore applications where combined loads are common in deep‐water environments. Their ultimate capacity under VH (vertical‐horizontal) combined loading is traditionally determined using VH failure envelopes, which are primarily constructed using numerical methods. These methodologies, however, frequently ignore the spatial variability inherent in seabed soils due to geological formations. This paper investigates the effect of spatial variability of undrained shear strength and embedment ratio impact on the capacity of skirted foundations subjected to VH combined loading. For this, OptumG2 software is used to perform Monte Carlo simulation combined with random finite element limit analysis. This paper investigates the stochastic analysis of bearing capacity and failure envelopes, with a particular emphasis on understanding the effect of spatial correlation on undrained shear strength. The study focuses on the horizontal scale of fluctuation and the soil strength heterogeneity index, shedding insight on previously undiscovered areas. Novel findings highlight how a rigid base affects VH failure envelopes and offer insights into evaluating the vertical bearing capacity of skirted foundations. [Correction added on 8 July 2024, after first online publication: The above statement has been updated in this version.]

Wave retrieval for Sentinel-1 synthetic aperture radar under complex sea state

ABSTRACT In this paper, sea surface waves during complex sea state (i.e. tropical cyclones (TCs) and Kuroshio current) are inverted from dual-polarized (vertical – vertical (VV) and vertical – horizontal (VH)) synthetic aperture radar (SAR) images using an existing algorithm, namely, the parameterized first-guess spectrum method (PFSM). In particular, the wind term is included in two modulation transfer functions (MTFs), i.e. tilt modulation and velocity bunching. More than 3000 Sentinel (S-1) images acquired in interferometric wide (IW) mode and extra wide (EW) mode are collected, and the operational CyclObs wind product provided by the French Research Institute for Exploitation of the Oceans (IFREMER) is collected corresponding to the TC images. Note that these images are located within 500 km away from TC eyes. The wave spectra during the TCs are then inverted using the PFSM algorithm and the SAR-derived wind product. Simultaneously, the 0.05° gridded wave spectra collocated with these images are simulated using a third-generation numeric wave model, called WAVEWATCH-III (WW3), in which the reconstructed wind based on the European Centre for Medium-Range Weather Forecasts (ECMWF), the Copernicus Marine Environment Monitoring Service (CMEMS) current, and the CMEMS sea level are treated as the forcing field. Comparison of the SAR wave retrievals and simulations reveals that the root mean square errors (RMSEs) of the significant wave height (SWH) and mean wave period (MWP) are 0.55 m and 0.43 s, their correlation coefficients (COR) are 0.94 and 0.92, and their scatter index (SI) values are 0.17 and 0.07. These results demonstrate the better performance of the PFSM algorithm using theoretical MTFs, i.e. an RMSE of 0.68 m and COR of 0.90 for the SWH and an RMSE of 0.60 s and a COR of 0.87 for the MWP. Although the accuracy of the wave retrieval from the SAR image under complex sea state is improved when implementing the tilt and velocity bunching MTFs with the strong wind term, SWH retrievals by algorithm PFSM suffer saturation problem at SWH > 8 m.

Influence and optimization of swirler parameters on elbow wear of deep coal fluidization pipeline transportation system

Elbow wear is a major threat to the multiphase pipeline transportation industry, which has a negative impact on the stable operation of the transportation system. In order to find the wear reduction methods of elbows in the fluidization pipeline transportation system for 2000-meter-deep coal resources, the swirler was installed upstream of the elbows, and wear simulations and tests of three kinds of elbows were carried out. The results showed that the maximum wear rate (MWR) of elbows increased and then decreased along the elbow angle. Due to the different directions of gravity, the heavy wear position (HWP) of the horizontal-vertical (H-V) elbow was in front of the vertical-horizontal (V-H) elbow. Because the downstream portion of the horizontal-horizontal (H-H) elbow was still a horizontal pipe, the HWP of the H-H elbow almost covered the whole elbow. The swirler placed upstream of the elbows could make the particles at the elbow move to the intrados of the elbows, resulting in less collision between the particles and the extrados of the elbows, thus reducing the wear of the elbows. The wear reduction effects of swirlers on three different elbows were favorably connected with the guide vane angle (GVA) and negatively correlated with the guide vane length (GVL), decreased first and then increased as the guide vane height (GVH) increased, and were little affected by the guide vane number (GVN) and the guide vane thickness (GVT). The mathematical models between the MWR of the elbows and guide vane parameters were established. By bench tests, the wear reduction effect of three kinds of elbows under the optimal guide vane parameters was 58.4%, 76.9%, and 78.6%, respectively. The errors between the bench test results and the simulation results were around 10%.

Analysis of wave breaking on synthetic aperture radar at C-band during tropical cyclones

ABSTRACT The purpose of our work is to analyze the effect of wave breaking on dual-polarized (vertical-vertical (VV) and vertical-horizontal (VH)) synthetic aperture radar (SAR) image in the C-band during tropical cyclones (TCs) based on the machine learning method. In this study, more than 1300 Sentinel-1 (S-1) interferometric-wide (IW) and extra wide (EW) mode SAR images are collocated with wave simulations from the WAVEWATCH-III (WW3) model during 400 TCs. The validation of the significant wave height (SWH) simulated using the WW3 model against Jason-2 altimeter data. The winds for S-1 SAR images are reconstructed using wind retrievals in VV and VH polarization. The non-polarized (NP) contribution σ wb caused by wave breaking is assumed to be the result of the SAR-measured normalized radar cross-section (NRCS) σ 0 minus the Bragg resonant roughness σ br without the distortion of rain cells during TCs. The σ br is simulated by imputing wave spectra from the WW3 model into the theoretical backscattering model. It is found that the ratio (σ wb /σ 0) in VV polarization is related to the wind speed, the wind direction relative with the flight orientation, and radar incidence angle. Following this rationale, the Adaptive Boosting (AdaBoost) model was used for the estimation of NP contribution σ wb during TCs and are implemented for more than 300 dual-polarized S-1 images to validate the model. It is found that for the comparison between the sum of simulation NRCS and SAR observations, the root mean squared error (RMSE) is 1.95 dB and the coefficient (COR) is 0.86, which is better than a 2.83 dB RMSE and a 0.67 COR by empirical model. It is concluded that the AdaBoost model has a good performance on NP component simulation during TCs.

Numerical study on undrained multi-directional bearing capacity of hybrid subsea foundation

The traditional foundation design does not consider the bearing capacity under multi-directional coupling load. However, the foundation is usually subjected to vertical (V), horizontal (H) and moment (M) loading simultaneously on the seabed. The present study examines the undrained multi-directional bearing capacity of hybrid subsea foundation in saturated soft clay using finite element analysis. The contribution of the mudmat and the suction bucket to the bearing capacity of the hybrid subsea foundation was evaluated. The results show that the suction bucket can effectively make up for the lack of horizontal anti-slip and anti-overturning ability of the mudmat. It has a significant contribution to the horizontal and moment bearing capacity. In addition, the failure envelope of bearing capacity in horizontal-moment (H-M), vertical-horizontal (V-H), and vertical-moment (V-M) load space is obtained by using prescribed displacement ratio loading method. Based on the simulation results, the expression of approximate envelope of foundation bearing capacity under combined load of horizontal and moment is proposed. The study also found that a certain value of vertical load can increase the horizontal and moment bearing capacity of the hybrid subsea foundation, while excessive vertical load will lead to a decrease in bearing capacity.

Use of Synthetic Aperture Radar (SAR) imagery on oil spill detection: A case study on the MV X-Press Pearl container ship incident in 2021 in Sri Lanka

Oil spills on the ocean from ships have a strong impact on the environment as well as on human societies. The MV X-Press Pearl container ship incident in 2021 became the worst marine environmental disaster in Sri Lanka due to its powerful impact. Oil leakage from the ship to the ocean continued for months. Synthetic Aperture Radar (SAR) imagery has the advantage of penetrating cloud cover, as well as day and night operating capabilities. Therefore, SAR imagery can be used to detect as well as observe the temporal changes of oil spills. The study in this article aimed to detect the oil spill caused by the MV X-Press Pearl container ship on Sri Lanka’s west coast from May to August 2021 using Sentinel-1 SAR data and measure the temporal changes of the oil slick. Three SAR imageries with Vertical-Vertical (VV) and Vertical-Horizontal (VH) polarization from June, July, and August of 2021 have been processed by applying the orbit file, running the multi-looking algorithm, and making the ellipsoid correction. The possible oil spills have been identified from each set of imagery as dark linear shapes with lengths of 3.1 ±0.012 km with an average reflectance of 15.6 decibels for June and 3.6 ±0.012 km with an average reflectance of 16.5 decibels for the other two months, and the shapes correspond with the oil slick classifications from other literature. The results indicate that the VV polarization is the most suitable to detect oil spills, and that the oil spill from the X-Press Pearl continued from June to the end of August 2021.

Extracting water body data based on SDWI and threshold segmentation: A case study in permafrost area surrounding Salt Lake in Hoh Xil, Qinghai-Tibet Plateau, China

There are a large number of lakes, rivers, and other natural water bodies distributed in the permafrost area of the Qinghai-Tibet Plateau (QTP). The changes in water bodies will affect the distribution of water resources in surrounding areas and downstream areas, resulting in environmental impact and bringing potential flood disasters, which will induce more serious issues and problems in alpine and high-altitude areas with a fragile habitat (such as the QTP in China). Generally, effective, reasonable, and scientific monitoring of large-scale water bodies can not only document the changes in water bodies intuitively, but also provide important theoretical reference for subsequent environmental impact prediction, and disaster prevention and mitigation in due course of time. The large-scale water extraction technology derived from the optical remote sensing (RS) image is seriously affected by clouds, bringing about large differences among the extracted water result products. Synthetic aperture radar (SAR) RS technology has the unique advantage characteristics of all-weather, all-day, strong penetration, and not being affected by clouds, which is hopeful in extracting water body data, especially for days with cloudy weather. The data extraction of large-scale water bodies based on SAR images can effectively avoid the errors caused by clouds that become prevalent at present. In this paper, the Hoh Xil Salt Lake on the QTP and its surrounding five lakes are taken as the research objects. The 2-scene Sentinel-1 SAR image data covering the whole area on 22 August 2022 was used to verify the feasibility of extracting water body data in permafrost zones. Furthermore, on 22 August 2022, the wealth here was cloudy, which made the optical RS images, e.g., Sentinel-2 images full of clouds. The results show that: using the Sentinel-1 image and threshold segmentation method to extract water body data is efficient and effective with excellent results in permafrost areas. Concretely, the Sentinel-1 dual-polarized water index (SDWI), calculated by combining dual vertical–vertical (VV) polarized and vertical–horizontal (VH) polarized data is a useful index for water extraction and the result is better than each of the VV or VH polarized images.

Wave retrieval from quad-polarized Chinese Gaofen-3 SAR image using an improved tilt modulation transfer function

ABSTRACT An accurate Modulation Transfer Function (MTF) is essential for Synthetic Aperture Radar (SAR) wave spectra retrieval. This study aimed to investigate the performance of a quad-polarized wave retrieval algorithm based on fully polarimetric SAR image data using the improved tilt MTF and considering the influence of wind speed. The tilt MTF is the key factor in the wave retrieval scheme from quad-polarized (Vertical–Vertical (VV), Horizontal–Horizontal (HH), Vertical–Horizontal (VH), and Horizontal–Vertical (HV)) SAR images. In this study, the waves were inverted from more than 1300 Gaofen-3 (GF-3) images acquired in quad-polarization strip mode with a spatial resolution of 16 m and a swath coverage of 50 km. The winds were retrieved using the Geophysical Model Function (GMF) C-band SAR model for Gaofen-3 (CSARMOD-GF), which is suitable for re-calibrated GF-3 images in VV-polarization. The comparison of the wind speed yielded a Root Mean Square Error (RMSE) of 1.73 m/s and a Correlation Coefficient (COR) of 0.94. The validation of the Significant Wave Height (SWH) simulated using the Waves Nearshore (SWAN) model against Haiyang-2B (HY-2B) altimeter data yielded an RMSE of 0.56 m and a COR of 0.87. The results reveal that the SAR-derived wind and SWAN-simulated SWH are suitable for analysis of SAR wave retrieval. The full polarimetric technique was applied to the collected images, and the statistical analysis yielded a RMSE of 0.51 m, a COR of 0.75, and a Scatter Index (SI) of 0.44 compared with the SWHs retrieved using the simulations from the SWAN model. The non-polarized contribution in the Normalized Radar Cross Section (NRCS; unit: dB) caused by wave breaking was calculated using a theoretical approach that employs the VV-polarized calibrated NRCS and HH-polarized calibrated NRCS. The effect of wave breaking on the SAR retrieval waves was studied. The bias (SAR-derived minus SWAN-simulated SWH) increased as the (/) ratio (>0.4) increased, and the accuracy improved when the ratio was less than 0.4. This behavior is reasonable since the wave breaking inevitably affects the tilt modulation. Therefore, wave breaking should be considered in SAR wave retrieval using the approach proposed in this paper under extreme sea states, such as typhoons and hurricanes.

Automatic lineaments detection using radar and optical data with an emphasis on geologic and tectonic implications: a case study of Kabul Block, eastern Afghanistan

The earth’s surface linear features, expressing geological lineaments, play a key role in identifying hydrothermal alteration and mineralization zones, as well as in understanding tectonic settings of a region. The objective of this investigation is to utilize a method for extracting lineaments automatically, which will be integrated and applied to identify geological-based lineaments by making use of remote sensing data. The study will further examine the structural pattern and temporal-spatial evolution of the lineaments and establish their connection with the primary active faults present in the Kabul Block. Multi-sensor data from radar (DEM (Digital Elevation Model)-5m, Sentinel-1B GRD (Ground Range Detected)) and optical sensors (Sentinel-2 MSI (Multispectral Instrument) and ASTER) were processed using spatial and spectral filters before automatic lineament extraction. LINE-module algorithm was applied to various illuminated hill-shades of DEM-5m, filtered HH (Horizonal-Horizonal) and VH (Vertical-Horizontal) of Sentinel-1GRD and PC1 (Principle Component) of Sentinel-2A MSI, and ASTER data to detect linear surface features. Extensive testing was conducted to verify the accuracy of extracted lineaments and to exclude any artificial lineaments in the study area. The radar and optical data results were compared while taking into consideration the geological and tectonic settings of the study area to select the most appropriate extracted lineaments. The DEM-5m and Sentinel-1B GRD showed the best result for identifying lineaments, and these were found to be highly correlated with previously available data in the Kabul Block. The final results of DEM-5m and Sentinel-1B GRD were further analyzed. The extracted lineaments were found to trend predominantly in a NW-SE and NE-SW directions, which is consistent with the results of other data. Temporal evolution and spatial distribution reveal that a high density of the lineaments is associated with Paleogene and Quaternary formations, while a low density is observed in Proterozoic, Paleozoic, and Mesozoic formations. The west and southwest edges of the Kabul Block are controlled by compressive stress trending NNW-SSE, while the north and southeast margins are influenced by NE-SW and ∼ N-S trending compressive stress.

Sentinel-1 time series data for sunflower (Helianthus annuus) phenology monitoring

The potential of time series of Sentinel-1 (S1) Synthetic Aperture Radar (SAR) data for monitoring crops and their phenological stages has long been recognized. However, sunflower (Helianthus annuus) is one of the crops whose interaction with microwave signal is still not fully understood. Here, we aim to analyze and interpret time series of S1 data for sunflower phenology monitoring. The SAR backscattering coefficients in VH (vertical-horizontal) and VV (vertical-vertical) polarizations as well as their ratio (VH/VV) in both ascending and descending orbits were retrieved for three sites located in three different agro-ecological regions in Ukraine. We observed that sunflower backscattering response differs for the ascending and descending orbits for the VV polarization and VH/VV polarization ratio due to the directional behavior of the flower head. Our analysis did not find major change in VH polarization for the two orbits. We also compared the S1-based sunflower phenology with that of Normalized Difference Vegetation Index (NDVI) derived from optical data acquired by Sentinel-2 (S2). Our analysis revealed a considerable correlation r2 > 0.4 between S1 and NDVI for VH and VV polarizations in both ascending and descending orbits; however, we observed lower correlation (r2 < 0.20) for the VH/VV polarization ratio in descending orbit (VH/VVdes). Based on the characteristics of sunflower phenology, we identified two sunflower phenological stages: beginning of flowering (BoF) and end of flowering (EoF). The difference between S1-based dates using VH/VVdes and reference dates was 4.1 and 4.4 days for the BoF and EoF stages, respectively; the use of VH/VVdes+asc yielded weaker results: 7.4 and 6.1 days. These results suggest that the VH/VVdes is a preferable parameter for identifying sunflower phenological stages such as BoF and EoF.

Influence of solid-liquid two-phase flow conveying parameters on the elbow wear with and without the swirler

Elbow wear is a considerable threat to the multiphase pipeline transportation industry. The swirler placed upstream of elbows could reduce wear. The dense discrete phase model (DDPM) was used to study the effects of different concentrations, particle sizes, and flow velocities on the wear of elbows with and without the swirler, and the formation mechanism of different wear morphologies was revealed. The mathematical models between conveying parameters and the MWR (maximum wear rate) were established, and the conveying parameters that were most suitable for using the swirler to reduce elbow wear were obtained. The bench test proved that the MWR of the horizontal-horizontal (H-H) elbow, horizontal-vertical (H-V) elbow, and vertical-horizontal (V-H) elbow could be reduced by 51.9%, 35.5%, and 23.2%.

Performance analysis of improved path loss models for millimeter-wave wireless network channels at 28 GHz and 38 GHz.

The importance of the path loss in millimeter wave channel propagation cannot be taken for granted in terms of deployment, design, performance assessment, and planning. The path loss helps to determine the network's geographic coverage. Although many path loss models, including statistical and empirical models based on measurement and linear regression, have been proposed by various researchers, high fidelity is required to determine the performance of the wireless network's channel. This research validates the improved version of the well-known close in (CI) and floating intercept (FI) path loss models at frequency bands of 28 and 38 GHz. The measurement surroundings comprised of an enclosed passageway with vertical-horizontal (V-H) and vertical-vertical (V-V) antenna polarizations. One of the key findings of this study is that the enhanced versions of these models typically perform better in terms of consistency than the standard models thereby justifying their high accuracy level. The improved versions of the CI and the FI models demonstrate a significant improvement for various antenna polarizations. The mean prediction error (MPE) and standard deviation error (SDE) also show how precisely and accurately the improved models predict the path loss. Additionally, the improved models provide the reasonable responsiveness and uniformity of the parameters with the change in the antenna polarization and lower the shadow fading's standard deviation in LOS as well as NLOS situations. The results confirm that the modified versions of CI and FI models predict path loss better in an enclosed environment for 5G networks.

Optimizing Wheat Yield Prediction Integrating Data from Sentinel-1 and Sentinel-2 with CatBoost Algorithm

Accurately estimating wheat yield is crucial for informed decision making in precision agriculture (PA) and improving crop management. In recent years, optical satellite-derived vegetation indices (Vis), such as Sentinel-2 (S2), have become widely used, but the availability of images depends on the weather conditions. For its part, Sentinel-1 (S1) backscatter data are less used in agriculture due to its complicated interpretation and processing, but is not impacted by weather. This study investigates the potential benefits of combining S1 and S2 data and evaluates the performance of the categorical boosting (CatBoost) algorithm in crop yield estimation. The study was conducted utilizing dense yield data from a yield monitor, obtained from 39 wheat (Triticum spp. L.) fields. The study analyzed three S2 images corresponding to different crop growth stages (GS) GS30, GS39-49, and GS69-75, and 13 Vis commonly used for wheat yield estimation were calculated for each image. In addition, three S1 images that were temporally close to the S2 images were acquired, and the vertical-vertical (VV) and vertical-horizontal (VH) backscatter were calculated. The performance of the CatBoost algorithm was compared to that of multiple linear regression (MLR), support vector machine (SVM), and random forest (RF) algorithms in crop yield estimation. The results showed that the combination of S1 and S2 data with the CatBoost algorithm produced a yield prediction with a root mean squared error (RMSE) of 0.24 t ha−1, a relative RMSE (rRMSE) 3.46% and an R2 of 0.95. The result indicates a decrease of 30% in RMSE when compared to using S2 alone. However, when this algorithm was used to estimate the yield of a whole plot, leveraging information from the surrounding plots, the mean absolute error (MAE) was 0.31 t ha−1 which means a mean error of 4.38%. Accurate wheat yield estimation with a spatial resolution of 10 m becomes feasible when utilizing satellite data combined with CatBoost.

Monitoring of Paddy and Maize Fields Using Sentinel-1 SAR Data and NGB Images: A Case Study in Papua, Indonesia

This study addresses the question of how to evaluate the growth stage of food crops, for instance, paddy (Oryza sativa) and maize (Zea mays), from two different sensors in selected developed areas of Papua Province of Indonesia. Level-1 Ground Range Detected (L1 GRD) images from Sentinel-1 Synthetic Aperture Radar (SAR) data were used to investigate the growth of paddy and maize crops. An NGB camera was then used to obtain the Green Normalized Difference Vegetation Index (GNDVI), and the Enhanced Normalized Difference Vegetation Index (ENDVI) as in situ measurement. Afterwards, the results were analyzed based on the Radar Vegetation Index (RVI) and the Vertical-Vertical (VV) and Vertical Horizontal (VH) band backscatters at incidence angles of 30.55°–45.88°, and 30.59°–46.16° in 2021 and 2022, respectively. The findings showed that Sigma0_VV_db and sigma0_VH_db had a strong correlation (R2 above 0.900); however, polarization modification is required, specifically in the maize field. The RVI calculated and backscatter changes in this study were comparable to the in situ measurements, specifically those of paddy fields, in 2022. Even though the results of this study were not able to prove the RVI values from the two relative orbits (orbit31 and orbit155) due to the different angle incidences and the availability of the Sentinel-1 SAR data set over the study area, the division of SAR image data based on each relative orbit adequately represents the development of crops in our study areas. The significance of this study is expected to support food crop security and the implementation of development plans that contribute to the local government’s goals and settings.

A novel change detection and threshold-based ensemble of scenarios pyramid for flood extent mapping using Sentinel-1 data

Flood disasters destroy infrastructure, disrupt ecosystem processes, adversely affect social and economic activities and cause human fatalities. As such, flood extent mapping (FEM) is critical to mitigate these impacts. Specifically, FEM is essential to mitigate adverse impacts through early warning, efficient response during evacuation, search, rescue and recovery. Furthermore, accurate FEM is crucial for policy formulation, planning and management, rehabilitation, and promoting community resilience for sustainable occupation and use of floodplains. Recently, remote sensing has become valuable in flood studies. However, whereas free passive remote sensing images have been common input into predictive models, damage assessment and FEM, their utility is constrained by clouds during flooding events. Conversely, microwave-based data is unconstrained by clouds, hence is important for FEM. Hence, to increase the reliability and accuracy of FEM using Sentinel-1 radar data, we propose a three-step process that builds an ensemble of scenarios pyramid (ESP) based on change detection and thresholding technique. We deployed the ESP technique and tested it on a use-case based on two, five and 10 images. The use-case calculated three co-polarized Vertical-Vertical (VV) and three cross-polarized Vertical-Horizontal (VH) normalized difference flood index scenarios to form six binary classified FEMs at the base. We ensembled the base scenarios to three dual-polarized centre FEMs, and likewise the centre scenarios to a final pinnacle flood extent map. The base, centre and pinnacle scenarios were validated using six binary classification performance metrics. The results show that the ESP increased the base-to-pinnacle minimum classification performance metrics with overall accuracy, Cohen's Kappa, intersect over union, recall, F1-score, and Matthews Correlation coefficient of 93.204%, 0.864, 0.865, 0.870, 0.927, and 0.871 respectively. The study also established that the VV channels were superior in FEM than VH at the ESP base. Overall, this study demonstrates the efficacy of the ESP for operational flood disaster management.

Assessment of Machine Learning Methods for Urban Types Classification Using Integrated SAR and Optical Images in Nonthaburi, Thailand

Urbanization and expansion in each city of emerging countries have become an essential function of Earth’s surface, with the majority of people migrating from rural to urban regions. The various urban category characteristics have emphasized the great importance of understanding and creating suitable land evaluations in the future. The overall objective of this study is to classify the urban zone utilizing building height which is estimated using Sentinel-1 synthetic aperture radar (SAR) and various satellite-based indexes of Sentinel-2A. The first objective of this research is to estimate the building height of the Sentinel-1 SAR in Nonthaburi, Thailand. A new indicator, vertical-vertical-horizontal polarization (VVH), which can provide a better performance, is produced from the dual-polarization information, vertical-vertical (VV), and vertical-horizontal (VH). Then, the building height model was developed using indicator VVH and the reference building height data. The root means square error (RMSE) between the estimated and reference height is 1.413 m. Then, the second objective is to classify three classes of urban types, which are composed of residential buildings, commercial buildings, and other buildings, including vegetation, waterbodies, car parks, and so on. Spectral indices such as normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and normalized difference built up the index (NDBI) are extracted from the Sentinel-2A data. To classify the urban types, a three-machine learning classifier, support vector machine (SVM), random forest (RF), and k-nearest neighbor (KNN) were developed. The classification uses randomly trained data from each 500 m focus study which are divided into a 100 × 100 m grid. Different models are examined using different variables, for example, classification using only building height and only spectral indices. The indices and estimated building height were used to classify the urban types. Not only the average of various satellite-based indices and building height of 100 × 100 m grid was used, but also the minimum, maximum, mean, and standard deviation were calculated from NDVI, NDWI, NDBI, and building height. There are a total of 16 variables used in the model. Eventually, the principal components analysis (PCA) was used to reduce the variables and get better performance of the models. SVM showed better accuracy than the other two, RF and KNN. The accuracies of SVM, RF, and KNN are 0.86, 0.75, and 0.76, respectively.